IOT-based service migration for connected communities. (December 2021)

• Record Type: Journal Article
• Authors: Rawashdeh, Majdi; Al Zamil, Mohammed G.H.; Samarah, Samer M.; Obaidat, Muath; Masud, Mehedi
• Is Part Of: Computers & electrical engineering. Volume 96:Part B(2021)
• Publication Date: 2021-12
• Subjects: 5G networks -- Cognitive robotics -- IoT for robotics -- Mobile agents -- Edge computing -- Service migration -- Cross-layering; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107530
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The limited power resources of edge-computing devices make it difficult to transfer their computational needs to different providers. Due to the high connectivity of the future 5G networks, the need for extra computing and storage resources will be increasing rapidly. The layered cloud-based computing architecture allows local nodes to share their computing and storage power. The presented methodology defines the migration process between a network and cloud-based architectures for delivering reliable, sustainable and expandable solutions. Abstract: Due to the nature of mobile systems in which devices change their locations continuously, there is a need for efficient techniques to integrate responses from different agents. Such techniques implement cross-layering service migration models to handle integration issues. This paper proposes a method that aims to model the migration process in an integrated architecture to resolve cross-layering issues, develop a dynamic policy, and optimize the cost of migration in terms of power consumption and communication. The proposed technique extends the Markovian model to adopt the spatiotemporal aspects of this problem, allowing modern 5G vertical applications to communicate seamlessly in highly connected IoT networks. Because of its dynamicity, we propose to project the environmental aspects into the observation part of HMM. We performed experiments to test the performance of the proposed method in terms of efficiency, reliability, and complexity. The results showed a significant performance in the mobile edge computing environment. Graphical abstract: Image, graphical abstract